*4.3. LSTM Feature Extraction*

Although CNN extracted rich feature representations, we doubt whether CNN can extract some critical hidden features having a long-time dependency. Based on this point, we employed LSTM to extract those features. Two-stacked LSTM layers are employed in this deep model, and every LSTM layer contains some LSTM cells as shown in Figure 1. The features LSTM extracted are expressed as Equation (22). *LSTM*() is the process of this sub-section.

$$\text{LSTM}\_{\text{features}} = \text{LSTM}(\text{Reshape}(\mathbf{x}(\mathbf{t}))) \tag{22}$$
